Overview

Dataset statistics

Number of variables43
Number of observations3881
Missing cells2934
Missing cells (%)1.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.9 MiB
Average record size in memory1.6 KiB

Variable types

Categorical30
Numeric10
DateTime3

Alerts

wlan_fc_type has constant value "2"Constant
_ws_col_cus_wlan_fc_type has constant value "Data frame"Constant
wlan_fc_type_subtype has constant value "0x0020"Constant
_ws_col_cus_wlan_fc_type_subtype has constant value "Data"Constant
wlan_bssid has constant value "02:00:00:00:01:00"Constant
wlan_ta has constant value "02:00:00:00:01:00"Constant
wlan_radio_channel has constant value "1"Constant
radiotap_channel_freq has constant value "2412"Constant
wlan_fc_retry has constant value "0"Constant
wlan_duration has constant value "0"Constant
wlan_fc has constant value "0x0802"Constant
wlan_fc_moredata has constant value "0"Constant
tcp_analysis_retransmission has constant value "1.0"Constant
wlan_radio_frequency has constant value "2412"Constant
radiotap_present_db_antnoise has constant value "0"Constant
radiotap_present_db_antsignal has constant value "0"Constant
radiotap_present_dbm_antnoise has constant value "0"Constant
radiotap_present_dbm_antsignal has constant value "0"Constant
analysisId has constant value "20240506172205"Constant
filePath has constant value "file-logDistance-exp3dot5-nt-91dBm-fc3-minspeed10-maxspeed20-area1600-testhttp_load.pcap"Constant
loadedOn has constant value "2024-05-13 14:35:06.849063"Constant
service_key has constant value "wifi_sharkfest"Constant
inserted_time has constant value "2024-05-13 14:35:52.128000+00:00"Constant
pipeline_version has constant value "0.0.105.7"Constant
label has constant value "0"Constant
_ws_col_cus_protocol is highly overall correlated with _ws_col_protocol and 5 other fieldsHigh correlation
_ws_col_protocol is highly overall correlated with _ws_col_cus_protocol and 5 other fieldsHigh correlation
frame_cap_len is highly overall correlated with frame_lenHigh correlation
frame_len is highly overall correlated with frame_cap_lenHigh correlation
frame_number is highly overall correlated with frame_time_epoch and 1 other fieldsHigh correlation
frame_protocols is highly overall correlated with _ws_col_cus_protocol and 5 other fieldsHigh correlation
frame_time_epoch is highly overall correlated with frame_number and 1 other fieldsHigh correlation
radiotap_datarate is highly overall correlated with wlan_radio_data_rate and 1 other fieldsHigh correlation
tcp_analysis_ack_rtt is highly overall correlated with wlan_addr and 3 other fieldsHigh correlation
wlan_addr is highly overall correlated with _ws_col_cus_protocol and 6 other fieldsHigh correlation
wlan_da is highly overall correlated with _ws_col_cus_protocol and 6 other fieldsHigh correlation
wlan_ra is highly overall correlated with _ws_col_cus_protocol and 6 other fieldsHigh correlation
wlan_radio_data_rate is highly overall correlated with radiotap_datarate and 1 other fieldsHigh correlation
wlan_radio_duration is highly overall correlated with radiotap_datarate and 1 other fieldsHigh correlation
wlan_sa is highly overall correlated with _ws_col_cus_protocol and 6 other fieldsHigh correlation
wlan_seq is highly overall correlated with frame_number and 1 other fieldsHigh correlation
_ws_col_cus_protocol is highly imbalanced (99.1%)Imbalance
_ws_col_protocol is highly imbalanced (99.1%)Imbalance
frame_protocols is highly imbalanced (99.1%)Imbalance
wlan_sa is highly imbalanced (99.4%)Imbalance
wlan_da is highly imbalanced (99.1%)Imbalance
wlan_ra is highly imbalanced (99.1%)Imbalance
wlan_addr is highly imbalanced (99.4%)Imbalance
tcp_analysis_ack_rtt has 2909 (75.0%) missing valuesMissing
frame_number is uniformly distributedUniform
wlan_seq is uniformly distributedUniform
frame_number has unique valuesUnique
frame_time_epoch has unique valuesUnique
wlan_seq has unique valuesUnique
timestamps has unique valuesUnique

Reproduction

Analysis started2024-06-07 14:35:35.992952
Analysis finished2024-06-07 14:35:48.630707
Duration12.64 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

_ws_col_cus_protocol
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size257.7 KiB
TCP
3878 
ICMPv6
 
3

Length

Max length6
Median length3
Mean length3.002319
Min length3

Characters and Unicode

Total characters11652
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTCP
2nd rowTCP
3rd rowTCP
4th rowTCP
5th rowTCP

Common Values

ValueCountFrequency (%)
TCP 3878
99.9%
ICMPv6 3
 
0.1%

Length

2024-06-07T10:35:48.727426image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T10:35:48.833930image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
tcp 3878
99.9%
icmpv6 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
C 3881
33.3%
P 3881
33.3%
T 3878
33.3%
I 3
 
< 0.1%
M 3
 
< 0.1%
v 3
 
< 0.1%
6 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 11646
99.9%
Lowercase Letter 3
 
< 0.1%
Decimal Number 3
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 3881
33.3%
P 3881
33.3%
T 3878
33.3%
I 3
 
< 0.1%
M 3
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
v 3
100.0%
Decimal Number
ValueCountFrequency (%)
6 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11649
> 99.9%
Common 3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 3881
33.3%
P 3881
33.3%
T 3878
33.3%
I 3
 
< 0.1%
M 3
 
< 0.1%
v 3
 
< 0.1%
Common
ValueCountFrequency (%)
6 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11652
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 3881
33.3%
P 3881
33.3%
T 3878
33.3%
I 3
 
< 0.1%
M 3
 
< 0.1%
v 3
 
< 0.1%
6 3
 
< 0.1%

_ws_col_protocol
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size257.7 KiB
TCP
3878 
ICMPv6
 
3

Length

Max length6
Median length3
Mean length3.002319
Min length3

Characters and Unicode

Total characters11652
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTCP
2nd rowTCP
3rd rowTCP
4th rowTCP
5th rowTCP

Common Values

ValueCountFrequency (%)
TCP 3878
99.9%
ICMPv6 3
 
0.1%

Length

2024-06-07T10:35:48.914496image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T10:35:48.983608image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
tcp 3878
99.9%
icmpv6 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
C 3881
33.3%
P 3881
33.3%
T 3878
33.3%
I 3
 
< 0.1%
M 3
 
< 0.1%
v 3
 
< 0.1%
6 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 11646
99.9%
Lowercase Letter 3
 
< 0.1%
Decimal Number 3
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 3881
33.3%
P 3881
33.3%
T 3878
33.3%
I 3
 
< 0.1%
M 3
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
v 3
100.0%
Decimal Number
ValueCountFrequency (%)
6 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11649
> 99.9%
Common 3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 3881
33.3%
P 3881
33.3%
T 3878
33.3%
I 3
 
< 0.1%
M 3
 
< 0.1%
v 3
 
< 0.1%
Common
ValueCountFrequency (%)
6 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11652
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 3881
33.3%
P 3881
33.3%
T 3878
33.3%
I 3
 
< 0.1%
M 3
 
< 0.1%
v 3
 
< 0.1%
6 3
 
< 0.1%

frame_protocols
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size379.0 KiB
radiotap:wlan_radio:wlan:llc:ip:tcp
3878 
radiotap:wlan_radio:wlan:llc:ipv6:icmpv6
 
3

Length

Max length40
Median length35
Mean length35.003865
Min length35

Characters and Unicode

Total characters135850
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowradiotap:wlan_radio:wlan:llc:ip:tcp
2nd rowradiotap:wlan_radio:wlan:llc:ip:tcp
3rd rowradiotap:wlan_radio:wlan:llc:ip:tcp
4th rowradiotap:wlan_radio:wlan:llc:ip:tcp
5th rowradiotap:wlan_radio:wlan:llc:ip:tcp

Common Values

ValueCountFrequency (%)
radiotap:wlan_radio:wlan:llc:ip:tcp 3878
99.9%
radiotap:wlan_radio:wlan:llc:ipv6:icmpv6 3
 
0.1%

Length

2024-06-07T10:35:49.065341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T10:35:49.139189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
radiotap:wlan_radio:wlan:llc:ip:tcp 3878
99.9%
radiotap:wlan_radio:wlan:llc:ipv6:icmpv6 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 19405
14.3%
: 19405
14.3%
l 15524
11.4%
i 11646
8.6%
p 11643
8.6%
r 7762
 
5.7%
d 7762
 
5.7%
o 7762
 
5.7%
w 7762
 
5.7%
n 7762
 
5.7%
Other values (6) 19417
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 112558
82.9%
Other Punctuation 19405
 
14.3%
Connector Punctuation 3881
 
2.9%
Decimal Number 6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 19405
17.2%
l 15524
13.8%
i 11646
10.3%
p 11643
10.3%
r 7762
 
6.9%
d 7762
 
6.9%
o 7762
 
6.9%
w 7762
 
6.9%
n 7762
 
6.9%
c 7762
 
6.9%
Other values (3) 7768
6.9%
Other Punctuation
ValueCountFrequency (%)
: 19405
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3881
100.0%
Decimal Number
ValueCountFrequency (%)
6 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 112558
82.9%
Common 23292
 
17.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 19405
17.2%
l 15524
13.8%
i 11646
10.3%
p 11643
10.3%
r 7762
 
6.9%
d 7762
 
6.9%
o 7762
 
6.9%
w 7762
 
6.9%
n 7762
 
6.9%
c 7762
 
6.9%
Other values (3) 7768
6.9%
Common
ValueCountFrequency (%)
: 19405
83.3%
_ 3881
 
16.7%
6 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 135850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 19405
14.3%
: 19405
14.3%
l 15524
11.4%
i 11646
8.6%
p 11643
8.6%
r 7762
 
5.7%
d 7762
 
5.7%
o 7762
 
5.7%
w 7762
 
5.7%
n 7762
 
5.7%
Other values (6) 19417
14.3%

frame_number
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct3881
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7390.0433
Minimum4865
Maximum9923
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.6 KiB
2024-06-07T10:35:49.245926image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4865
5-th percentile5109
Q16123
median7392
Q38658
95-th percentile9674
Maximum9923
Range5058
Interquartile range (IQR)2535

Descriptive statistics

Standard deviation1464.4985
Coefficient of variation (CV)0.19817185
Kurtosis-1.2001378
Mean7390.0433
Median Absolute Deviation (MAD)1268
Skewness0.00037306215
Sum28680758
Variance2144755.9
MonotonicityStrictly increasing
2024-06-07T10:35:49.350553image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4865 1
 
< 0.1%
8262 1
 
< 0.1%
8224 1
 
< 0.1%
8225 1
 
< 0.1%
8231 1
 
< 0.1%
8232 1
 
< 0.1%
8233 1
 
< 0.1%
8234 1
 
< 0.1%
8235 1
 
< 0.1%
8236 1
 
< 0.1%
Other values (3871) 3871
99.7%
ValueCountFrequency (%)
4865 1
< 0.1%
4866 1
< 0.1%
4868 1
< 0.1%
4869 1
< 0.1%
4870 1
< 0.1%
4871 1
< 0.1%
4872 1
< 0.1%
4873 1
< 0.1%
4874 1
< 0.1%
4875 1
< 0.1%
ValueCountFrequency (%)
9923 1
< 0.1%
9922 1
< 0.1%
9921 1
< 0.1%
9920 1
< 0.1%
9918 1
< 0.1%
9917 1
< 0.1%
9916 1
< 0.1%
9915 1
< 0.1%
9914 1
< 0.1%
9913 1
< 0.1%

frame_time_epoch
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct3881
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7153815 × 109
Minimum1.7153815 × 109
Maximum1.7153816 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.6 KiB
2024-06-07T10:35:49.452995image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.7153815 × 109
5-th percentile1.7153815 × 109
Q11.7153815 × 109
median1.7153815 × 109
Q31.7153816 × 109
95-th percentile1.7153816 × 109
Maximum1.7153816 × 109
Range120.06616
Interquartile range (IQR)60.754635

Descriptive statistics

Standard deviation35.118965
Coefficient of variation (CV)2.0472976 × 10-8
Kurtosis-1.2015051
Mean1.7153815 × 109
Median Absolute Deviation (MAD)30.620219
Skewness0.0013275534
Sum6.6573958 × 1012
Variance1233.3417
MonotonicityStrictly increasing
2024-06-07T10:35:49.560280image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1715381481 1
 
< 0.1%
1715381561 1
 
< 0.1%
1715381561 1
 
< 0.1%
1715381561 1
 
< 0.1%
1715381561 1
 
< 0.1%
1715381561 1
 
< 0.1%
1715381561 1
 
< 0.1%
1715381561 1
 
< 0.1%
1715381561 1
 
< 0.1%
1715381561 1
 
< 0.1%
Other values (3871) 3871
99.7%
ValueCountFrequency (%)
1715381481 1
< 0.1%
1715381481 1
< 0.1%
1715381481 1
< 0.1%
1715381481 1
< 0.1%
1715381481 1
< 0.1%
1715381481 1
< 0.1%
1715381481 1
< 0.1%
1715381481 1
< 0.1%
1715381481 1
< 0.1%
1715381481 1
< 0.1%
ValueCountFrequency (%)
1715381601 1
< 0.1%
1715381601 1
< 0.1%
1715381601 1
< 0.1%
1715381601 1
< 0.1%
1715381601 1
< 0.1%
1715381601 1
< 0.1%
1715381601 1
< 0.1%
1715381601 1
< 0.1%
1715381601 1
< 0.1%
1715381601 1
< 0.1%

wlan_fc_type
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size250.1 KiB
2
3881 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3881
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 3881
100.0%

Length

2024-06-07T10:35:49.654434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T10:35:49.790103image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2 3881
100.0%

Most occurring characters

ValueCountFrequency (%)
2 3881
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3881
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 3881
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3881
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 3881
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3881
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 3881
100.0%

_ws_col_cus_wlan_fc_type
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size284.3 KiB
Data frame
3881 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters38810
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowData frame
2nd rowData frame
3rd rowData frame
4th rowData frame
5th rowData frame

Common Values

ValueCountFrequency (%)
Data frame 3881
100.0%

Length

2024-06-07T10:35:49.870670image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T10:35:49.937349image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
data 3881
50.0%
frame 3881
50.0%

Most occurring characters

ValueCountFrequency (%)
a 11643
30.0%
D 3881
 
10.0%
t 3881
 
10.0%
3881
 
10.0%
f 3881
 
10.0%
r 3881
 
10.0%
m 3881
 
10.0%
e 3881
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31048
80.0%
Uppercase Letter 3881
 
10.0%
Space Separator 3881
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 11643
37.5%
t 3881
 
12.5%
f 3881
 
12.5%
r 3881
 
12.5%
m 3881
 
12.5%
e 3881
 
12.5%
Uppercase Letter
ValueCountFrequency (%)
D 3881
100.0%
Space Separator
ValueCountFrequency (%)
3881
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 34929
90.0%
Common 3881
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 11643
33.3%
D 3881
 
11.1%
t 3881
 
11.1%
f 3881
 
11.1%
r 3881
 
11.1%
m 3881
 
11.1%
e 3881
 
11.1%
Common
ValueCountFrequency (%)
3881
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 11643
30.0%
D 3881
 
10.0%
t 3881
 
10.0%
3881
 
10.0%
f 3881
 
10.0%
r 3881
 
10.0%
m 3881
 
10.0%
e 3881
 
10.0%

wlan_fc_type_subtype
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size269.1 KiB
0x0020
3881 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters23286
Distinct characters3
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0x0020
2nd row0x0020
3rd row0x0020
4th row0x0020
5th row0x0020

Common Values

ValueCountFrequency (%)
0x0020 3881
100.0%

Length

2024-06-07T10:35:50.029066image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T10:35:50.137922image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0x0020 3881
100.0%

Most occurring characters

ValueCountFrequency (%)
0 15524
66.7%
x 3881
 
16.7%
2 3881
 
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19405
83.3%
Lowercase Letter 3881
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15524
80.0%
2 3881
 
20.0%
Lowercase Letter
ValueCountFrequency (%)
x 3881
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19405
83.3%
Latin 3881
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15524
80.0%
2 3881
 
20.0%
Latin
ValueCountFrequency (%)
x 3881
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23286
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15524
66.7%
x 3881
 
16.7%
2 3881
 
16.7%

_ws_col_cus_wlan_fc_type_subtype
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size261.5 KiB
Data
3881 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters15524
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowData
2nd rowData
3rd rowData
4th rowData
5th rowData

Common Values

ValueCountFrequency (%)
Data 3881
100.0%

Length

2024-06-07T10:35:50.225229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T10:35:50.303497image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
data 3881
100.0%

Most occurring characters

ValueCountFrequency (%)
a 7762
50.0%
D 3881
25.0%
t 3881
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11643
75.0%
Uppercase Letter 3881
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 7762
66.7%
t 3881
33.3%
Uppercase Letter
ValueCountFrequency (%)
D 3881
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15524
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 7762
50.0%
D 3881
25.0%
t 3881
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15524
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 7762
50.0%
D 3881
25.0%
t 3881
25.0%

wlan_bssid
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.8 KiB
02:00:00:00:01:00
3881 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters65977
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row02:00:00:00:01:00
2nd row02:00:00:00:01:00
3rd row02:00:00:00:01:00
4th row02:00:00:00:01:00
5th row02:00:00:00:01:00

Common Values

ValueCountFrequency (%)
02:00:00:00:01:00 3881
100.0%

Length

2024-06-07T10:35:50.366610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T10:35:50.436383image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
02:00:00:00:01:00 3881
100.0%

Most occurring characters

ValueCountFrequency (%)
0 38810
58.8%
: 19405
29.4%
2 3881
 
5.9%
1 3881
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46572
70.6%
Other Punctuation 19405
29.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38810
83.3%
2 3881
 
8.3%
1 3881
 
8.3%
Other Punctuation
ValueCountFrequency (%)
: 19405
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 65977
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38810
58.8%
: 19405
29.4%
2 3881
 
5.9%
1 3881
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65977
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38810
58.8%
: 19405
29.4%
2 3881
 
5.9%
1 3881
 
5.9%

wlan_sa
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size310.8 KiB
92:de:7b:2d:75:2f
3879 
02:00:00:00:01:00
 
2

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters65977
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row92:de:7b:2d:75:2f
2nd row92:de:7b:2d:75:2f
3rd row92:de:7b:2d:75:2f
4th row92:de:7b:2d:75:2f
5th row92:de:7b:2d:75:2f

Common Values

ValueCountFrequency (%)
92:de:7b:2d:75:2f 3879
99.9%
02:00:00:00:01:00 2
 
0.1%

Length

2024-06-07T10:35:50.521086image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T10:35:50.631267image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
92:de:7b:2d:75:2f 3879
99.9%
02:00:00:00:01:00 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
: 19405
29.4%
2 11639
17.6%
d 7758
 
11.8%
7 7758
 
11.8%
9 3879
 
5.9%
e 3879
 
5.9%
b 3879
 
5.9%
5 3879
 
5.9%
f 3879
 
5.9%
0 20
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27177
41.2%
Other Punctuation 19405
29.4%
Lowercase Letter 19395
29.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 11639
42.8%
7 7758
28.5%
9 3879
 
14.3%
5 3879
 
14.3%
0 20
 
0.1%
1 2
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
d 7758
40.0%
e 3879
20.0%
b 3879
20.0%
f 3879
20.0%
Other Punctuation
ValueCountFrequency (%)
: 19405
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 46582
70.6%
Latin 19395
29.4%

Most frequent character per script

Common
ValueCountFrequency (%)
: 19405
41.7%
2 11639
25.0%
7 7758
 
16.7%
9 3879
 
8.3%
5 3879
 
8.3%
0 20
 
< 0.1%
1 2
 
< 0.1%
Latin
ValueCountFrequency (%)
d 7758
40.0%
e 3879
20.0%
b 3879
20.0%
f 3879
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65977
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 19405
29.4%
2 11639
17.6%
d 7758
 
11.8%
7 7758
 
11.8%
9 3879
 
5.9%
e 3879
 
5.9%
b 3879
 
5.9%
5 3879
 
5.9%
f 3879
 
5.9%
0 20
 
< 0.1%

wlan_da
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size310.8 KiB
00:00:00:00:00:02
3878 
33:33:00:00:00:02
 
3

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters65977
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00:00:00:00:00:02
2nd row00:00:00:00:00:02
3rd row00:00:00:00:00:02
4th row00:00:00:00:00:02
5th row00:00:00:00:00:02

Common Values

ValueCountFrequency (%)
00:00:00:00:00:02 3878
99.9%
33:33:00:00:00:02 3
 
0.1%

Length

2024-06-07T10:35:50.738866image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T10:35:50.815591image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00:00:00:00:02 3878
99.9%
33:33:00:00:00:02 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 42679
64.7%
: 19405
29.4%
2 3881
 
5.9%
3 12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46572
70.6%
Other Punctuation 19405
29.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 42679
91.6%
2 3881
 
8.3%
3 12
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
: 19405
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 65977
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 42679
64.7%
: 19405
29.4%
2 3881
 
5.9%
3 12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65977
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 42679
64.7%
: 19405
29.4%
2 3881
 
5.9%
3 12
 
< 0.1%

wlan_ra
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size310.8 KiB
00:00:00:00:00:02
3878 
33:33:00:00:00:02
 
3

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters65977
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00:00:00:00:00:02
2nd row00:00:00:00:00:02
3rd row00:00:00:00:00:02
4th row00:00:00:00:00:02
5th row00:00:00:00:00:02

Common Values

ValueCountFrequency (%)
00:00:00:00:00:02 3878
99.9%
33:33:00:00:00:02 3
 
0.1%

Length

2024-06-07T10:35:51.001664image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T10:35:51.088813image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00:00:00:00:02 3878
99.9%
33:33:00:00:00:02 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 42679
64.7%
: 19405
29.4%
2 3881
 
5.9%
3 12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46572
70.6%
Other Punctuation 19405
29.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 42679
91.6%
2 3881
 
8.3%
3 12
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
: 19405
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 65977
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 42679
64.7%
: 19405
29.4%
2 3881
 
5.9%
3 12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65977
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 42679
64.7%
: 19405
29.4%
2 3881
 
5.9%
3 12
 
< 0.1%

wlan_ta
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.8 KiB
02:00:00:00:01:00
3881 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters65977
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row02:00:00:00:01:00
2nd row02:00:00:00:01:00
3rd row02:00:00:00:01:00
4th row02:00:00:00:01:00
5th row02:00:00:00:01:00

Common Values

ValueCountFrequency (%)
02:00:00:00:01:00 3881
100.0%

Length

2024-06-07T10:35:51.203846image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T10:35:51.290969image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
02:00:00:00:01:00 3881
100.0%

Most occurring characters

ValueCountFrequency (%)
0 38810
58.8%
: 19405
29.4%
2 3881
 
5.9%
1 3881
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46572
70.6%
Other Punctuation 19405
29.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38810
83.3%
2 3881
 
8.3%
1 3881
 
8.3%
Other Punctuation
ValueCountFrequency (%)
: 19405
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 65977
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38810
58.8%
: 19405
29.4%
2 3881
 
5.9%
1 3881
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65977
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38810
58.8%
: 19405
29.4%
2 3881
 
5.9%
1 3881
 
5.9%

wlan_radio_channel
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size250.1 KiB
1
3881 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3881
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 3881
100.0%

Length

2024-06-07T10:35:51.373866image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T10:35:51.448085image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3881
100.0%

Most occurring characters

ValueCountFrequency (%)
1 3881
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3881
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3881
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3881
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3881
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3881
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3881
100.0%

wlan_radio_data_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.152538
Minimum1
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.6 KiB
2024-06-07T10:35:51.517609image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median12
Q336
95-th percentile54
Maximum54
Range53
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.331134
Coefficient of variation (CV)0.90490015
Kurtosis-0.58087586
Mean19.152538
Median Absolute Deviation (MAD)6.5
Skewness0.90392395
Sum74331
Variance300.36822
MonotonicityNot monotonic
2024-06-07T10:35:51.624265image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
18 370
9.5%
54 348
9.0%
1 318
8.2%
48 317
8.2%
36 317
8.2%
6 317
8.2%
9 316
8.1%
12 316
8.1%
2 316
8.1%
5.5 316
8.1%
Other values (2) 630
16.2%
ValueCountFrequency (%)
1 318
8.2%
2 316
8.1%
5.5 316
8.1%
6 317
8.2%
9 316
8.1%
11 315
8.1%
12 316
8.1%
18 370
9.5%
24 315
8.1%
36 317
8.2%
ValueCountFrequency (%)
54 348
9.0%
48 317
8.2%
36 317
8.2%
24 315
8.1%
18 370
9.5%
12 316
8.1%
11 315
8.1%
9 316
8.1%
6 317
8.2%
5.5 316
8.1%

radiotap_channel_freq
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size261.5 KiB
2412
3881 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters15524
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2412
2nd row2412
3rd row2412
4th row2412
5th row2412

Common Values

ValueCountFrequency (%)
2412 3881
100.0%

Length

2024-06-07T10:35:51.716791image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T10:35:51.799843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2412 3881
100.0%

Most occurring characters

ValueCountFrequency (%)
2 7762
50.0%
4 3881
25.0%
1 3881
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15524
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 7762
50.0%
4 3881
25.0%
1 3881
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15524
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 7762
50.0%
4 3881
25.0%
1 3881
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15524
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 7762
50.0%
4 3881
25.0%
1 3881
25.0%

radiotap_datarate
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.152538
Minimum1
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.6 KiB
2024-06-07T10:35:51.880478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median12
Q336
95-th percentile54
Maximum54
Range53
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.331134
Coefficient of variation (CV)0.90490015
Kurtosis-0.58087586
Mean19.152538
Median Absolute Deviation (MAD)6.5
Skewness0.90392395
Sum74331
Variance300.36822
MonotonicityNot monotonic
2024-06-07T10:35:51.971748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
18 370
9.5%
54 348
9.0%
1 318
8.2%
48 317
8.2%
36 317
8.2%
6 317
8.2%
9 316
8.1%
12 316
8.1%
2 316
8.1%
5.5 316
8.1%
Other values (2) 630
16.2%
ValueCountFrequency (%)
1 318
8.2%
2 316
8.1%
5.5 316
8.1%
6 317
8.2%
9 316
8.1%
11 315
8.1%
12 316
8.1%
18 370
9.5%
24 315
8.1%
36 317
8.2%
ValueCountFrequency (%)
54 348
9.0%
48 317
8.2%
36 317
8.2%
24 315
8.1%
18 370
9.5%
12 316
8.1%
11 315
8.1%
9 316
8.1%
6 317
8.2%
5.5 316
8.1%

wlan_addr
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size447.2 KiB
00:00:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f
3878 
33:33:00:00:00:02$02:00:00:00:01:00$02:00:00:00:01:00
 
2
33:33:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f
 
1

Length

Max length53
Median length53
Mean length53
Min length53

Characters and Unicode

Total characters205693
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row00:00:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f
2nd row00:00:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f
3rd row00:00:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f
4th row00:00:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f
5th row00:00:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f

Common Values

ValueCountFrequency (%)
00:00:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f 3878
99.9%
33:33:00:00:00:02$02:00:00:00:01:00$02:00:00:00:01:00 2
 
0.1%
33:33:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f 1
 
< 0.1%

Length

2024-06-07T10:35:52.128885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T10:35:52.208201image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f 3878
99.9%
33:33:00:00:00:02$02:00:00:00:01:00$02:00:00:00:01:00 2
 
0.1%
33:33:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 81509
39.6%
: 58215
28.3%
2 19401
 
9.4%
$ 7762
 
3.8%
d 7758
 
3.8%
7 7758
 
3.8%
1 3883
 
1.9%
9 3879
 
1.9%
e 3879
 
1.9%
b 3879
 
1.9%
Other values (3) 7770
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 120321
58.5%
Other Punctuation 58215
28.3%
Lowercase Letter 19395
 
9.4%
Currency Symbol 7762
 
3.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 81509
67.7%
2 19401
 
16.1%
7 7758
 
6.4%
1 3883
 
3.2%
9 3879
 
3.2%
5 3879
 
3.2%
3 12
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
d 7758
40.0%
e 3879
20.0%
b 3879
20.0%
f 3879
20.0%
Other Punctuation
ValueCountFrequency (%)
: 58215
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 7762
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 186298
90.6%
Latin 19395
 
9.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 81509
43.8%
: 58215
31.2%
2 19401
 
10.4%
$ 7762
 
4.2%
7 7758
 
4.2%
1 3883
 
2.1%
9 3879
 
2.1%
5 3879
 
2.1%
3 12
 
< 0.1%
Latin
ValueCountFrequency (%)
d 7758
40.0%
e 3879
20.0%
b 3879
20.0%
f 3879
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 205693
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 81509
39.6%
: 58215
28.3%
2 19401
 
9.4%
$ 7762
 
3.8%
d 7758
 
3.8%
7 7758
 
3.8%
1 3883
 
1.9%
9 3879
 
1.9%
e 3879
 
1.9%
b 3879
 
1.9%
Other values (3) 7770
 
3.8%

wlan_seq
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct3881
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1987.2517
Minimum46
Maximum3928
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.6 KiB
2024-06-07T10:35:52.333648image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile240
Q11017
median1987
Q32958
95-th percentile3734
Maximum3928
Range3882
Interquartile range (IQR)1941

Descriptive statistics

Standard deviation1121.0189
Coefficient of variation (CV)0.56410512
Kurtosis-1.1998444
Mean1987.2517
Median Absolute Deviation (MAD)971
Skewness3.3835296 × 10-5
Sum7712524
Variance1256683.3
MonotonicityStrictly increasing
2024-06-07T10:35:52.458272image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 1
 
< 0.1%
2655 1
 
< 0.1%
2627 1
 
< 0.1%
2628 1
 
< 0.1%
2629 1
 
< 0.1%
2630 1
 
< 0.1%
2631 1
 
< 0.1%
2632 1
 
< 0.1%
2633 1
 
< 0.1%
2634 1
 
< 0.1%
Other values (3871) 3871
99.7%
ValueCountFrequency (%)
46 1
< 0.1%
47 1
< 0.1%
48 1
< 0.1%
49 1
< 0.1%
50 1
< 0.1%
51 1
< 0.1%
52 1
< 0.1%
53 1
< 0.1%
54 1
< 0.1%
55 1
< 0.1%
ValueCountFrequency (%)
3928 1
< 0.1%
3927 1
< 0.1%
3926 1
< 0.1%
3925 1
< 0.1%
3924 1
< 0.1%
3923 1
< 0.1%
3922 1
< 0.1%
3921 1
< 0.1%
3920 1
< 0.1%
3919 1
< 0.1%

wlan_fc_retry
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size250.1 KiB
0
3881 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3881
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3881
100.0%

Length

2024-06-07T10:35:52.581654image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T10:35:53.419739image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3881
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3881
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3881
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3881
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3881
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3881
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3881
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3881
100.0%

wlan_duration
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size250.1 KiB
0
3881 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3881
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3881
100.0%

Length

2024-06-07T10:35:53.495680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T10:35:53.564234image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3881
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3881
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3881
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3881
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3881
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3881
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3881
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3881
100.0%

wlan_fc
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size269.1 KiB
0x0802
3881 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters23286
Distinct characters4
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0x0802
2nd row0x0802
3rd row0x0802
4th row0x0802
5th row0x0802

Common Values

ValueCountFrequency (%)
0x0802 3881
100.0%

Length

2024-06-07T10:35:53.642371image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T10:35:53.711773image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0x0802 3881
100.0%

Most occurring characters

ValueCountFrequency (%)
0 11643
50.0%
x 3881
 
16.7%
8 3881
 
16.7%
2 3881
 
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19405
83.3%
Lowercase Letter 3881
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11643
60.0%
8 3881
 
20.0%
2 3881
 
20.0%
Lowercase Letter
ValueCountFrequency (%)
x 3881
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19405
83.3%
Latin 3881
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11643
60.0%
8 3881
 
20.0%
2 3881
 
20.0%
Latin
ValueCountFrequency (%)
x 3881
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23286
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11643
50.0%
x 3881
 
16.7%
8 3881
 
16.7%
2 3881
 
16.7%

wlan_fc_moredata
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size250.1 KiB
0
3881 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3881
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3881
100.0%

Length

2024-06-07T10:35:53.781850image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T10:35:53.851500image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3881
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3881
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3881
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3881
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3881
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3881
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3881
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3881
100.0%

frame_len
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1426.3576
Minimum106
Maximum1554
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.6 KiB
2024-06-07T10:35:53.914706image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum106
5-th percentile1306
Q11306
median1554
Q31554
95-th percentile1554
Maximum1554
Range1448
Interquartile range (IQR)248

Descriptive statistics

Standard deviation167.47091
Coefficient of variation (CV)0.11741158
Kurtosis22.501875
Mean1426.3576
Median Absolute Deviation (MAD)0
Skewness-3.2585668
Sum5535694
Variance28046.505
MonotonicityNot monotonic
2024-06-07T10:35:53.995816image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1554 2018
52.0%
1306 874
22.5%
1326 635
 
16.4%
1325 151
 
3.9%
1324 88
 
2.3%
1078 65
 
1.7%
106 18
 
0.5%
1077 17
 
0.4%
1076 8
 
0.2%
114 4
 
0.1%
ValueCountFrequency (%)
106 18
 
0.5%
110 3
 
0.1%
114 4
 
0.1%
1076 8
 
0.2%
1077 17
 
0.4%
1078 65
 
1.7%
1306 874
22.5%
1324 88
 
2.3%
1325 151
 
3.9%
1326 635
16.4%
ValueCountFrequency (%)
1554 2018
52.0%
1326 635
 
16.4%
1325 151
 
3.9%
1324 88
 
2.3%
1306 874
22.5%
1078 65
 
1.7%
1077 17
 
0.4%
1076 8
 
0.2%
114 4
 
0.1%
110 3
 
0.1%

wlan_radio_duration
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2204.7483
Minimum36
Maximum12448
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.6 KiB
2024-06-07T10:35:54.090486image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile216
Q1364
median1044
Q32068
95-th percentile10624
Maximum12448
Range12412
Interquartile range (IQR)1704

Descriptive statistics

Standard deviation3163.2444
Coefficient of variation (CV)1.4347418
Kurtosis3.8268781
Mean2204.7483
Median Absolute Deviation (MAD)732
Skewness2.2072836
Sum8556628
Variance10006115
MonotonicityNot monotonic
2024-06-07T10:35:54.206506image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
704 231
 
6.0%
6320 184
 
4.7%
1044 181
 
4.7%
12448 173
 
4.5%
532 171
 
4.4%
2421 169
 
4.4%
2068 168
 
4.3%
248 160
 
4.1%
1307 158
 
4.1%
364 156
 
4.0%
Other values (60) 2130
54.9%
ValueCountFrequency (%)
36 4
 
0.1%
40 2
 
0.1%
60 10
 
0.3%
64 4
 
0.1%
100 1
 
< 0.1%
180 1
 
< 0.1%
200 5
 
0.1%
212 95
2.4%
216 89
2.3%
236 112
2.9%
ValueCountFrequency (%)
12448 173
4.5%
10624 44
 
1.1%
10616 9
 
0.2%
10608 6
 
0.2%
10464 75
1.9%
8640 6
 
0.2%
8632 2
 
0.1%
6320 184
4.7%
5408 48
 
1.2%
5404 10
 
0.3%

frame_cap_len
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1426.3576
Minimum106
Maximum1554
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.6 KiB
2024-06-07T10:35:54.286833image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum106
5-th percentile1306
Q11306
median1554
Q31554
95-th percentile1554
Maximum1554
Range1448
Interquartile range (IQR)248

Descriptive statistics

Standard deviation167.47091
Coefficient of variation (CV)0.11741158
Kurtosis22.501875
Mean1426.3576
Median Absolute Deviation (MAD)0
Skewness-3.2585668
Sum5535694
Variance28046.505
MonotonicityNot monotonic
2024-06-07T10:35:54.356342image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1554 2018
52.0%
1306 874
22.5%
1326 635
 
16.4%
1325 151
 
3.9%
1324 88
 
2.3%
1078 65
 
1.7%
106 18
 
0.5%
1077 17
 
0.4%
1076 8
 
0.2%
114 4
 
0.1%
ValueCountFrequency (%)
106 18
 
0.5%
110 3
 
0.1%
114 4
 
0.1%
1076 8
 
0.2%
1077 17
 
0.4%
1078 65
 
1.7%
1306 874
22.5%
1324 88
 
2.3%
1325 151
 
3.9%
1326 635
16.4%
ValueCountFrequency (%)
1554 2018
52.0%
1326 635
 
16.4%
1325 151
 
3.9%
1324 88
 
2.3%
1306 874
22.5%
1078 65
 
1.7%
1077 17
 
0.4%
1076 8
 
0.2%
114 4
 
0.1%
110 3
 
0.1%

tcp_analysis_ack_rtt
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct732
Distinct (%)75.3%
Missing2909
Missing (%)75.0%
Infinite0
Infinite (%)0.0%
Mean1.7153587 × 109
Minimum1.7153587 × 109
Maximum1.7153587 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.6 KiB
2024-06-07T10:35:54.447553image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.7153587 × 109
5-th percentile1.7153587 × 109
Q11.7153587 × 109
median1.7153587 × 109
Q31.7153587 × 109
95-th percentile1.7153587 × 109
Maximum1.7153587 × 109
Range0.0040650368
Interquartile range (IQR)0.00091028214

Descriptive statistics

Standard deviation0.000625411
Coefficient of variation (CV)3.6459488 × 10-13
Kurtosis0.76827312
Mean1.7153587 × 109
Median Absolute Deviation (MAD)0.00039303303
Skewness0.98574366
Sum1.6673286 × 1012
Variance3.9113891 × 10-7
MonotonicityNot monotonic
2024-06-07T10:35:54.569357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1715358681 7
 
0.2%
1715358681 5
 
0.1%
1715358681 5
 
0.1%
1715358681 5
 
0.1%
1715358681 4
 
0.1%
1715358681 4
 
0.1%
1715358681 4
 
0.1%
1715358681 4
 
0.1%
1715358681 4
 
0.1%
1715358681 4
 
0.1%
Other values (722) 926
 
23.9%
(Missing) 2909
75.0%
ValueCountFrequency (%)
1715358681 1
< 0.1%
1715358681 1
< 0.1%
1715358681 1
< 0.1%
1715358681 1
< 0.1%
1715358681 1
< 0.1%
1715358681 1
< 0.1%
1715358681 1
< 0.1%
1715358681 1
< 0.1%
1715358681 1
< 0.1%
1715358681 1
< 0.1%
ValueCountFrequency (%)
1715358681 1
< 0.1%
1715358681 1
< 0.1%
1715358681 1
< 0.1%
1715358681 1
< 0.1%
1715358681 1
< 0.1%
1715358681 1
< 0.1%
1715358681 1
< 0.1%
1715358681 1
< 0.1%
1715358681 1
< 0.1%
1715358681 1
< 0.1%

tcp_analysis_retransmission
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing25
Missing (%)0.6%
Memory size257.0 KiB
1.0
3856 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11568
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 3856
99.4%
(Missing) 25
 
0.6%

Length

2024-06-07T10:35:54.674094image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T10:35:54.738590image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3856
100.0%

Most occurring characters

ValueCountFrequency (%)
1 3856
33.3%
. 3856
33.3%
0 3856
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7712
66.7%
Other Punctuation 3856
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3856
50.0%
0 3856
50.0%
Other Punctuation
ValueCountFrequency (%)
. 3856
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11568
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3856
33.3%
. 3856
33.3%
0 3856
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11568
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3856
33.3%
. 3856
33.3%
0 3856
33.3%

frame_time_delta
Real number (ℝ)

Distinct1489
Distinct (%)38.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0047543064
Minimum1 × 10-6
Maximum0.10233
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.6 KiB
2024-06-07T10:35:54.833367image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1 × 10-6
5-th percentile2 × 10-6
Q13 × 10-6
median1.1 × 10-5
Q30.002619
95-th percentile0.020449
Maximum0.10233
Range0.102329
Interquartile range (IQR)0.002616

Descriptive statistics

Standard deviation0.014355282
Coefficient of variation (CV)3.0194272
Kurtosis21.700186
Mean0.0047543064
Median Absolute Deviation (MAD)1 × 10-5
Skewness4.5422255
Sum18.451463
Variance0.00020607412
MonotonicityNot monotonic
2024-06-07T10:35:54.944710image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 × 10-6635
 
16.4%
3 × 10-6496
 
12.8%
4 × 10-6177
 
4.6%
1 × 10-6166
 
4.3%
7 × 10-6103
 
2.7%
8 × 10-691
 
2.3%
6 × 10-686
 
2.2%
5 × 10-673
 
1.9%
9 × 10-661
 
1.6%
1 × 10-544
 
1.1%
Other values (1479) 1949
50.2%
ValueCountFrequency (%)
1 × 10-6166
 
4.3%
2 × 10-6635
16.4%
3 × 10-6496
12.8%
4 × 10-6177
 
4.6%
5 × 10-673
 
1.9%
6 × 10-686
 
2.2%
7 × 10-6103
 
2.7%
8 × 10-691
 
2.3%
9 × 10-661
 
1.6%
1 × 10-544
 
1.1%
ValueCountFrequency (%)
0.10233 1
< 0.1%
0.1023 1
< 0.1%
0.102133 1
< 0.1%
0.099721 1
< 0.1%
0.099557 1
< 0.1%
0.09949 1
< 0.1%
0.099094 1
< 0.1%
0.098994 1
< 0.1%
0.098864 1
< 0.1%
0.098829 1
< 0.1%

wlan_radio_frequency
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size261.5 KiB
2412
3881 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters15524
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2412
2nd row2412
3rd row2412
4th row2412
5th row2412

Common Values

ValueCountFrequency (%)
2412 3881
100.0%

Length

2024-06-07T10:35:55.046629image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T10:35:55.116807image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2412 3881
100.0%

Most occurring characters

ValueCountFrequency (%)
2 7762
50.0%
4 3881
25.0%
1 3881
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15524
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 7762
50.0%
4 3881
25.0%
1 3881
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15524
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 7762
50.0%
4 3881
25.0%
1 3881
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15524
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 7762
50.0%
4 3881
25.0%
1 3881
25.0%

radiotap_present_db_antnoise
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size250.1 KiB
0
3881 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3881
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3881
100.0%

Length

2024-06-07T10:35:55.194942image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T10:35:55.265883image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3881
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3881
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3881
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3881
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3881
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3881
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3881
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3881
100.0%

radiotap_present_db_antsignal
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size250.1 KiB
0
3881 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3881
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3881
100.0%

Length

2024-06-07T10:35:55.336455image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T10:35:55.434565image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3881
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3881
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3881
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3881
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3881
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3881
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3881
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3881
100.0%

radiotap_present_dbm_antnoise
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size250.1 KiB
0
3881 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3881
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3881
100.0%

Length

2024-06-07T10:35:55.550356image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T10:35:55.632034image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3881
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3881
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3881
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3881
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3881
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3881
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3881
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3881
100.0%

radiotap_present_dbm_antsignal
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size250.1 KiB
0
3881 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3881
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3881
100.0%

Length

2024-06-07T10:35:55.706934image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T10:35:55.776724image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3881
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3881
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3881
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3881
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3881
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3881
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3881
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3881
100.0%

analysisId
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size299.4 KiB
20240506172205
3881 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters54334
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20240506172205
2nd row20240506172205
3rd row20240506172205
4th row20240506172205
5th row20240506172205

Common Values

ValueCountFrequency (%)
20240506172205 3881
100.0%

Length

2024-06-07T10:35:55.856406image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T10:35:55.930862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
20240506172205 3881
100.0%

Most occurring characters

ValueCountFrequency (%)
2 15524
28.6%
0 15524
28.6%
5 7762
14.3%
4 3881
 
7.1%
6 3881
 
7.1%
1 3881
 
7.1%
7 3881
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 54334
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 15524
28.6%
0 15524
28.6%
5 7762
14.3%
4 3881
 
7.1%
6 3881
 
7.1%
1 3881
 
7.1%
7 3881
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Common 54334
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 15524
28.6%
0 15524
28.6%
5 7762
14.3%
4 3881
 
7.1%
6 3881
 
7.1%
1 3881
 
7.1%
7 3881
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54334
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 15524
28.6%
0 15524
28.6%
5 7762
14.3%
4 3881
 
7.1%
6 3881
 
7.1%
1 3881
 
7.1%
7 3881
 
7.1%

filePath
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size579.9 KiB
file-logDistance-exp3dot5-nt-91dBm-fc3-minspeed10-maxspeed20-area1600-testhttp_load.pcap
3881 

Length

Max length88
Median length88
Mean length88
Min length88

Characters and Unicode

Total characters341528
Distinct characters29
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfile-logDistance-exp3dot5-nt-91dBm-fc3-minspeed10-maxspeed20-area1600-testhttp_load.pcap
2nd rowfile-logDistance-exp3dot5-nt-91dBm-fc3-minspeed10-maxspeed20-area1600-testhttp_load.pcap
3rd rowfile-logDistance-exp3dot5-nt-91dBm-fc3-minspeed10-maxspeed20-area1600-testhttp_load.pcap
4th rowfile-logDistance-exp3dot5-nt-91dBm-fc3-minspeed10-maxspeed20-area1600-testhttp_load.pcap
5th rowfile-logDistance-exp3dot5-nt-91dBm-fc3-minspeed10-maxspeed20-area1600-testhttp_load.pcap

Common Values

ValueCountFrequency (%)
file-logDistance-exp3dot5-nt-91dBm-fc3-minspeed10-maxspeed20-area1600-testhttp_load.pcap 3881
100.0%

Length

2024-06-07T10:35:56.017757image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T10:35:56.090357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
file-logdistance-exp3dot5-nt-91dbm-fc3-minspeed10-maxspeed20-area1600-testhttp_load.pcap 3881
100.0%

Most occurring characters

ValueCountFrequency (%)
e 34929
 
10.2%
- 34929
 
10.2%
t 27167
 
8.0%
p 23286
 
6.8%
a 23286
 
6.8%
d 19405
 
5.7%
0 15524
 
4.5%
s 15524
 
4.5%
l 11643
 
3.4%
o 11643
 
3.4%
Other values (19) 124192
36.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 240622
70.5%
Decimal Number 50453
 
14.8%
Dash Punctuation 34929
 
10.2%
Uppercase Letter 7762
 
2.3%
Connector Punctuation 3881
 
1.1%
Other Punctuation 3881
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 34929
14.5%
t 27167
11.3%
p 23286
9.7%
a 23286
9.7%
d 19405
 
8.1%
s 15524
 
6.5%
l 11643
 
4.8%
o 11643
 
4.8%
m 11643
 
4.8%
n 11643
 
4.8%
Other values (7) 50453
21.0%
Decimal Number
ValueCountFrequency (%)
0 15524
30.8%
1 11643
23.1%
3 7762
15.4%
5 3881
 
7.7%
9 3881
 
7.7%
2 3881
 
7.7%
6 3881
 
7.7%
Uppercase Letter
ValueCountFrequency (%)
B 3881
50.0%
D 3881
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 34929
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3881
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3881
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 248384
72.7%
Common 93144
 
27.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 34929
14.1%
t 27167
10.9%
p 23286
 
9.4%
a 23286
 
9.4%
d 19405
 
7.8%
s 15524
 
6.2%
l 11643
 
4.7%
o 11643
 
4.7%
m 11643
 
4.7%
n 11643
 
4.7%
Other values (9) 58215
23.4%
Common
ValueCountFrequency (%)
- 34929
37.5%
0 15524
16.7%
1 11643
 
12.5%
3 7762
 
8.3%
5 3881
 
4.2%
9 3881
 
4.2%
2 3881
 
4.2%
6 3881
 
4.2%
_ 3881
 
4.2%
. 3881
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 341528
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 34929
 
10.2%
- 34929
 
10.2%
t 27167
 
8.0%
p 23286
 
6.8%
a 23286
 
6.8%
d 19405
 
5.7%
0 15524
 
4.5%
s 15524
 
4.5%
l 11643
 
3.4%
o 11643
 
3.4%
Other values (19) 124192
36.4%

loadedOn
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.6 KiB
Minimum2024-05-13 14:35:06.849063
Maximum2024-05-13 14:35:06.849063
2024-06-07T10:35:56.163022image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:56.230704image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

service_key
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size299.4 KiB
wifi_sharkfest
3881 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters54334
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwifi_sharkfest
2nd rowwifi_sharkfest
3rd rowwifi_sharkfest
4th rowwifi_sharkfest
5th rowwifi_sharkfest

Common Values

ValueCountFrequency (%)
wifi_sharkfest 3881
100.0%

Length

2024-06-07T10:35:56.318670image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T10:35:56.386844image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
wifi_sharkfest 3881
100.0%

Most occurring characters

ValueCountFrequency (%)
i 7762
14.3%
f 7762
14.3%
s 7762
14.3%
w 3881
7.1%
_ 3881
7.1%
h 3881
7.1%
a 3881
7.1%
r 3881
7.1%
k 3881
7.1%
e 3881
7.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 50453
92.9%
Connector Punctuation 3881
 
7.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 7762
15.4%
f 7762
15.4%
s 7762
15.4%
w 3881
7.7%
h 3881
7.7%
a 3881
7.7%
r 3881
7.7%
k 3881
7.7%
e 3881
7.7%
t 3881
7.7%
Connector Punctuation
ValueCountFrequency (%)
_ 3881
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 50453
92.9%
Common 3881
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 7762
15.4%
f 7762
15.4%
s 7762
15.4%
w 3881
7.7%
h 3881
7.7%
a 3881
7.7%
r 3881
7.7%
k 3881
7.7%
e 3881
7.7%
t 3881
7.7%
Common
ValueCountFrequency (%)
_ 3881
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54334
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 7762
14.3%
f 7762
14.3%
s 7762
14.3%
w 3881
7.1%
_ 3881
7.1%
h 3881
7.1%
a 3881
7.1%
r 3881
7.1%
k 3881
7.1%
e 3881
7.1%

inserted_time
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.6 KiB
Minimum2024-05-13 14:35:52.128000+00:00
Maximum2024-05-13 14:35:52.128000+00:00
2024-06-07T10:35:56.486347image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:56.585762image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

pipeline_version
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size280.5 KiB
0.0.105.7
3881 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters34929
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0.105.7
2nd row0.0.105.7
3rd row0.0.105.7
4th row0.0.105.7
5th row0.0.105.7

Common Values

ValueCountFrequency (%)
0.0.105.7 3881
100.0%

Length

2024-06-07T10:35:56.698894image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T10:35:56.767681image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0.105.7 3881
100.0%

Most occurring characters

ValueCountFrequency (%)
0 11643
33.3%
. 11643
33.3%
1 3881
 
11.1%
5 3881
 
11.1%
7 3881
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23286
66.7%
Other Punctuation 11643
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11643
50.0%
1 3881
 
16.7%
5 3881
 
16.7%
7 3881
 
16.7%
Other Punctuation
ValueCountFrequency (%)
. 11643
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34929
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11643
33.3%
. 11643
33.3%
1 3881
 
11.1%
5 3881
 
11.1%
7 3881
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34929
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11643
33.3%
. 11643
33.3%
1 3881
 
11.1%
5 3881
 
11.1%
7 3881
 
11.1%

label
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size250.1 KiB
0
3881 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3881
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3881
100.0%

Length

2024-06-07T10:35:56.838959image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T10:35:56.907058image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3881
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3881
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3881
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3881
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3881
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3881
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3881
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3881
100.0%

timestamps
Date

UNIQUE 

Distinct3881
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size60.6 KiB
Minimum2024-05-10 22:51:21.115257
Maximum2024-05-10 22:53:21.181422
2024-06-07T10:35:56.989904image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:57.107446image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-06-07T10:35:46.459503image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:37.731025image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:38.557929image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:39.468052image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:40.524798image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:41.487439image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:42.437340image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:43.256006image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:44.288263image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:45.208676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:46.562538image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:37.827558image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:38.629241image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:39.580729image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:40.601466image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:41.564371image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:42.514751image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:43.334610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:44.396828image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:45.295406image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:46.659986image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:37.922105image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:38.727644image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:39.712096image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:40.879059image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:41.685962image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:42.603460image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:43.417624image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:44.500931image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:45.389978image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:46.759818image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:37.995838image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:38.801511image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:39.857426image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:40.947750image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:41.798009image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:42.684501image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:43.489502image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:44.589122image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:45.479633image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:46.841436image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:38.078870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:38.899239image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:39.941663image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:41.021563image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:41.872008image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:42.755065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:43.575487image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:44.664768image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:45.566277image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:46.927577image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:38.159875image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:39.035668image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:40.072488image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:41.100109image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:41.956609image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:42.837575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:43.675042image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:44.747527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:45.909854image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:47.015293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:38.236383image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:39.118914image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:40.186475image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:41.171119image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:42.043191image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:42.916238image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:43.768593image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:44.827390image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:45.999333image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:47.116740image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:38.313119image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:39.209086image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:40.275502image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:41.249619image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:42.139755image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:42.991450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:43.930216image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:44.934077image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:46.092319image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:47.213356image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:38.393248image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:39.287111image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:40.347903image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:41.320998image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:42.221302image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:43.090688image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:44.046730image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:45.008902image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:46.202481image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:47.388923image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:38.487786image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:39.391878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:40.448869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:41.409736image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:42.334822image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:43.175898image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:44.172283image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:45.108862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-07T10:35:46.339875image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-06-07T10:35:57.199157image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
_ws_col_cus_protocol_ws_col_protocolframe_cap_lenframe_lenframe_numberframe_protocolsframe_time_deltaframe_time_epochradiotap_dataratetcp_analysis_ack_rttwlan_addrwlan_dawlan_rawlan_radio_data_ratewlan_radio_durationwlan_sawlan_seq
_ws_col_cus_protocol1.0000.8330.0520.0520.0110.833-0.0440.0110.044NaN1.0000.8330.8330.0440.0030.6120.011
_ws_col_protocol0.8331.0000.0520.0520.0110.833-0.0440.0110.044NaN1.0000.8330.8330.0440.0030.6120.011
frame_cap_len0.0520.0521.0001.0000.0250.3440.3340.025-0.033-0.0220.2430.3440.344-0.0330.1610.2810.025
frame_len0.0520.0521.0001.0000.0250.3440.3340.025-0.033-0.0220.2430.3440.344-0.0330.1610.2810.025
frame_number0.0110.0110.0250.0251.0000.0000.0011.000-0.0310.0780.0000.0000.000-0.0310.0380.0001.000
frame_protocols0.8330.8330.3440.3440.0001.0000.044-0.011-0.044NaN1.0000.8330.833-0.044-0.0030.612-0.011
frame_time_delta-0.044-0.0440.3340.3340.0010.0441.0000.001-0.050-0.1700.2300.3290.329-0.0500.0810.2670.001
frame_time_epoch0.0110.0110.0250.0251.000-0.0110.0011.000-0.0310.0780.0000.0000.000-0.0310.0380.0001.000
radiotap_datarate0.0440.044-0.033-0.033-0.031-0.044-0.050-0.0311.000-0.0990.0000.0000.0001.000-0.9860.000-0.031
tcp_analysis_ack_rttNaNNaN-0.022-0.0220.078NaN-0.1700.078-0.0991.0001.0001.0001.000-0.0990.0981.0000.078
wlan_addr1.0001.0000.2430.2430.0001.0000.2300.0000.0001.0001.0001.0001.000-0.044-0.0031.000-0.011
wlan_da0.8330.8330.3440.3440.0000.8330.3290.0000.0001.0001.0001.0000.833-0.044-0.0030.612-0.011
wlan_ra0.8330.8330.3440.3440.0000.8330.3290.0000.0001.0001.0000.8331.000-0.044-0.0030.612-0.011
wlan_radio_data_rate0.0440.044-0.033-0.033-0.031-0.044-0.050-0.0311.000-0.099-0.044-0.044-0.0441.000-0.9860.000-0.031
wlan_radio_duration0.0030.0030.1610.1610.038-0.0030.0810.038-0.9860.098-0.003-0.003-0.003-0.9861.0000.0000.038
wlan_sa0.6120.6120.2810.2810.0000.6120.2670.0000.0001.0001.0000.6120.6120.0000.0001.0000.011
wlan_seq0.0110.0110.0250.0251.000-0.0110.0011.000-0.0310.078-0.011-0.011-0.011-0.0310.0380.0111.000

Missing values

2024-06-07T10:35:47.614591image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-07T10:35:48.233247image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-06-07T10:35:48.560861image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

_ws_col_cus_protocol_ws_col_protocolframe_protocolsframe_numberframe_time_epochwlan_fc_type_ws_col_cus_wlan_fc_typewlan_fc_type_subtype_ws_col_cus_wlan_fc_type_subtypewlan_bssidwlan_sawlan_dawlan_rawlan_tawlan_radio_channelwlan_radio_data_rateradiotap_channel_freqradiotap_dataratewlan_addrwlan_seqwlan_fc_retrywlan_durationwlan_fcwlan_fc_moredataframe_lenwlan_radio_durationframe_cap_lentcp_analysis_ack_rtttcp_analysis_retransmissionframe_time_deltawlan_radio_frequencyradiotap_present_db_antnoiseradiotap_present_db_antsignalradiotap_present_dbm_antnoiseradiotap_present_dbm_antsignalanalysisIdfilePathloadedOnservice_keyinserted_timepipeline_versionlabeltimestamps
4857TCPTCPradiotap:wlan_radio:wlan:llc:ip:tcp48651.715381e+092Data frame0x0020Data02:00:00:00:01:0092:de:7b:2d:75:2f00:00:00:00:00:0200:00:00:00:00:0202:00:00:00:01:00118.0241218.000:00:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f46.0000x0802011464.01141.715359e+09NaN0.0010572412000020240506172205file-logDistance-exp3dot5-nt-91dBm-fc3-minspeed10-maxspeed20-area1600-testhttp_load.pcap2024-05-13T14:35:06.849063wifi_sharkfest2024-05-13T14:35:52.128Z0.0.105.702024-05-10 22:51:21.115257856
4858TCPTCPradiotap:wlan_radio:wlan:llc:ip:tcp48661.715381e+092Data frame0x0020Data02:00:00:00:01:0092:de:7b:2d:75:2f00:00:00:00:00:0200:00:00:00:00:0202:00:00:00:01:00118.0241218.000:00:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f47.0000x0802011464.01141.715359e+09NaN0.0000312412000020240506172205file-logDistance-exp3dot5-nt-91dBm-fc3-minspeed10-maxspeed20-area1600-testhttp_load.pcap2024-05-13T14:35:06.849063wifi_sharkfest2024-05-13T14:35:52.128Z0.0.105.702024-05-10 22:51:21.115289088
4860TCPTCPradiotap:wlan_radio:wlan:llc:ip:tcp48681.715381e+092Data frame0x0020Data02:00:00:00:01:0092:de:7b:2d:75:2f00:00:00:00:00:0200:00:00:00:00:0202:00:00:00:01:00118.0241218.000:00:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f48.0000x0802011464.01141.715359e+09NaN0.0005072412000020240506172205file-logDistance-exp3dot5-nt-91dBm-fc3-minspeed10-maxspeed20-area1600-testhttp_load.pcap2024-05-13T14:35:06.849063wifi_sharkfest2024-05-13T14:35:52.128Z0.0.105.702024-05-10 22:51:21.115952128
4861TCPTCPradiotap:wlan_radio:wlan:llc:ip:tcp48691.715381e+092Data frame0x0020Data02:00:00:00:01:0092:de:7b:2d:75:2f00:00:00:00:00:0200:00:00:00:00:0202:00:00:00:01:00118.0241218.000:00:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f49.0000x0802011464.01141.715359e+09NaN0.0003802412000020240506172205file-logDistance-exp3dot5-nt-91dBm-fc3-minspeed10-maxspeed20-area1600-testhttp_load.pcap2024-05-13T14:35:06.849063wifi_sharkfest2024-05-13T14:35:52.128Z0.0.105.702024-05-10 22:51:21.116332032
4862TCPTCPradiotap:wlan_radio:wlan:llc:ip:tcp48701.715381e+092Data frame0x0020Data02:00:00:00:01:0092:de:7b:2d:75:2f00:00:00:00:00:0200:00:00:00:00:0202:00:00:00:01:00118.0241218.000:00:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f50.0000x0802010660.01061.715359e+09NaN0.0005952412000020240506172205file-logDistance-exp3dot5-nt-91dBm-fc3-minspeed10-maxspeed20-area1600-testhttp_load.pcap2024-05-13T14:35:06.849063wifi_sharkfest2024-05-13T14:35:52.128Z0.0.105.702024-05-10 22:51:21.116926976
4863TCPTCPradiotap:wlan_radio:wlan:llc:ip:tcp48711.715381e+092Data frame0x0020Data02:00:00:00:01:0092:de:7b:2d:75:2f00:00:00:00:00:0200:00:00:00:00:0202:00:00:00:01:00148.0241248.000:00:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f51.0000x080201554276.01554NaN1.00.0002972412000020240506172205file-logDistance-exp3dot5-nt-91dBm-fc3-minspeed10-maxspeed20-area1600-testhttp_load.pcap2024-05-13T14:35:06.849063wifi_sharkfest2024-05-13T14:35:52.128Z0.0.105.702024-05-10 22:51:21.117223936
4864TCPTCPradiotap:wlan_radio:wlan:llc:ip:tcp48721.715381e+092Data frame0x0020Data02:00:00:00:01:0092:de:7b:2d:75:2f00:00:00:00:00:0200:00:00:00:00:0202:00:00:00:01:00118.0241218.000:00:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f52.0000x080201554704.01554NaN1.00.0000332412000020240506172205file-logDistance-exp3dot5-nt-91dBm-fc3-minspeed10-maxspeed20-area1600-testhttp_load.pcap2024-05-13T14:35:06.849063wifi_sharkfest2024-05-13T14:35:52.128Z0.0.105.702024-05-10 22:51:21.117257216
4865TCPTCPradiotap:wlan_radio:wlan:llc:ip:tcp48731.715381e+092Data frame0x0020Data02:00:00:00:01:0092:de:7b:2d:75:2f00:00:00:00:00:0200:00:00:00:00:0202:00:00:00:01:00118.0241218.000:00:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f53.0000x080201306592.01306NaN1.00.0000362412000020240506172205file-logDistance-exp3dot5-nt-91dBm-fc3-minspeed10-maxspeed20-area1600-testhttp_load.pcap2024-05-13T14:35:06.849063wifi_sharkfest2024-05-13T14:35:52.128Z0.0.105.702024-05-10 22:51:21.117292800
4866TCPTCPradiotap:wlan_radio:wlan:llc:ip:tcp48741.715381e+092Data frame0x0020Data02:00:00:00:01:0092:de:7b:2d:75:2f00:00:00:00:00:0200:00:00:00:00:0202:00:00:00:01:00118.0241218.000:00:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f54.0000x080201325604.01325NaN1.00.0000392412000020240506172205file-logDistance-exp3dot5-nt-91dBm-fc3-minspeed10-maxspeed20-area1600-testhttp_load.pcap2024-05-13T14:35:06.849063wifi_sharkfest2024-05-13T14:35:52.128Z0.0.105.702024-05-10 22:51:21.117331968
4867TCPTCPradiotap:wlan_radio:wlan:llc:ip:tcp48751.715381e+092Data frame0x0020Data02:00:00:00:01:0092:de:7b:2d:75:2f00:00:00:00:00:0200:00:00:00:00:0202:00:00:00:01:00118.0241218.000:00:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f55.0000x080201554704.015541.715359e+091.00.0034592412000020240506172205file-logDistance-exp3dot5-nt-91dBm-fc3-minspeed10-maxspeed20-area1600-testhttp_load.pcap2024-05-13T14:35:06.849063wifi_sharkfest2024-05-13T14:35:52.128Z0.0.105.702024-05-10 22:51:21.120791040
_ws_col_cus_protocol_ws_col_protocolframe_protocolsframe_numberframe_time_epochwlan_fc_type_ws_col_cus_wlan_fc_typewlan_fc_type_subtype_ws_col_cus_wlan_fc_type_subtypewlan_bssidwlan_sawlan_dawlan_rawlan_tawlan_radio_channelwlan_radio_data_rateradiotap_channel_freqradiotap_dataratewlan_addrwlan_seqwlan_fc_retrywlan_durationwlan_fcwlan_fc_moredataframe_lenwlan_radio_durationframe_cap_lentcp_analysis_ack_rtttcp_analysis_retransmissionframe_time_deltawlan_radio_frequencyradiotap_present_db_antnoiseradiotap_present_db_antsignalradiotap_present_dbm_antnoiseradiotap_present_dbm_antsignalanalysisIdfilePathloadedOnservice_keyinserted_timepipeline_versionlabeltimestamps
9097TCPTCPradiotap:wlan_radio:wlan:llc:ip:tcp99131.715382e+092Data frame0x0020Data02:00:00:00:01:0092:de:7b:2d:75:2f00:00:00:00:00:0200:00:00:00:00:0202:00:00:00:01:00112.0241212.000:00:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f3919.0000x0802015541044.01554NaN1.00.0000022412000020240506172205file-logDistance-exp3dot5-nt-91dBm-fc3-minspeed10-maxspeed20-area1600-testhttp_load.pcap2024-05-13T14:35:06.849063wifi_sharkfest2024-05-13T14:35:52.128Z0.0.105.702024-05-10 22:53:21.133570048
9098TCPTCPradiotap:wlan_radio:wlan:llc:ip:tcp99141.715382e+092Data frame0x0020Data02:00:00:00:01:0092:de:7b:2d:75:2f00:00:00:00:00:0200:00:00:00:00:0202:00:00:00:01:0015.524125.500:00:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f3920.0000x0802013062060.01306NaN1.00.0000032412000020240506172205file-logDistance-exp3dot5-nt-91dBm-fc3-minspeed10-maxspeed20-area1600-testhttp_load.pcap2024-05-13T14:35:06.849063wifi_sharkfest2024-05-13T14:35:52.128Z0.0.105.702024-05-10 22:53:21.133573120
9099TCPTCPradiotap:wlan_radio:wlan:llc:ip:tcp99151.715382e+092Data frame0x0020Data02:00:00:00:01:0092:de:7b:2d:75:2f00:00:00:00:00:0200:00:00:00:00:0202:00:00:00:01:00124.0241224.000:00:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f3921.0000x080201325456.01325NaN1.00.0142762412000020240506172205file-logDistance-exp3dot5-nt-91dBm-fc3-minspeed10-maxspeed20-area1600-testhttp_load.pcap2024-05-13T14:35:06.849063wifi_sharkfest2024-05-13T14:35:52.128Z0.0.105.702024-05-10 22:53:21.147848960
9100TCPTCPradiotap:wlan_radio:wlan:llc:ip:tcp99161.715382e+092Data frame0x0020Data02:00:00:00:01:0092:de:7b:2d:75:2f00:00:00:00:00:0200:00:00:00:00:0202:00:00:00:01:00148.0241248.000:00:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f3922.0000x080201325240.01325NaN1.00.0134692412000020240506172205file-logDistance-exp3dot5-nt-91dBm-fc3-minspeed10-maxspeed20-area1600-testhttp_load.pcap2024-05-13T14:35:06.849063wifi_sharkfest2024-05-13T14:35:52.128Z0.0.105.702024-05-10 22:53:21.161318144
9101TCPTCPradiotap:wlan_radio:wlan:llc:ip:tcp99171.715382e+092Data frame0x0020Data02:00:00:00:01:0092:de:7b:2d:75:2f00:00:00:00:00:0200:00:00:00:00:0202:00:00:00:01:0016.024126.000:00:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f3923.0000x0802013251764.01325NaN1.00.0031722412000020240506172205file-logDistance-exp3dot5-nt-91dBm-fc3-minspeed10-maxspeed20-area1600-testhttp_load.pcap2024-05-13T14:35:06.849063wifi_sharkfest2024-05-13T14:35:52.128Z0.0.105.702024-05-10 22:53:21.164489984
9102TCPTCPradiotap:wlan_radio:wlan:llc:ip:tcp99181.715382e+092Data frame0x0020Data02:00:00:00:01:0092:de:7b:2d:75:2f00:00:00:00:00:0200:00:00:00:00:0202:00:00:00:01:00118.0241218.000:00:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f3924.0000x080201325604.01325NaN1.00.0092042412000020240506172205file-logDistance-exp3dot5-nt-91dBm-fc3-minspeed10-maxspeed20-area1600-testhttp_load.pcap2024-05-13T14:35:06.849063wifi_sharkfest2024-05-13T14:35:52.128Z0.0.105.702024-05-10 22:53:21.173693952
9104TCPTCPradiotap:wlan_radio:wlan:llc:ip:tcp99201.715382e+092Data frame0x0020Data02:00:00:00:01:0092:de:7b:2d:75:2f00:00:00:00:00:0200:00:00:00:00:0202:00:00:00:01:00136.0241236.000:00:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f3925.0000x0802010640.01061.715359e+09NaN0.0032832412000020240506172205file-logDistance-exp3dot5-nt-91dBm-fc3-minspeed10-maxspeed20-area1600-testhttp_load.pcap2024-05-13T14:35:06.849063wifi_sharkfest2024-05-13T14:35:52.128Z0.0.105.702024-05-10 22:53:21.180890112
9105TCPTCPradiotap:wlan_radio:wlan:llc:ip:tcp99211.715382e+092Data frame0x0020Data02:00:00:00:01:0092:de:7b:2d:75:2f00:00:00:00:00:0200:00:00:00:00:0202:00:00:00:01:0015.524125.500:00:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f3926.0000x08020106315.01061.715359e+09NaN0.0003702412000020240506172205file-logDistance-exp3dot5-nt-91dBm-fc3-minspeed10-maxspeed20-area1600-testhttp_load.pcap2024-05-13T14:35:06.849063wifi_sharkfest2024-05-13T14:35:52.128Z0.0.105.702024-05-10 22:53:21.181260032
9106TCPTCPradiotap:wlan_radio:wlan:llc:ip:tcp99221.715382e+092Data frame0x0020Data02:00:00:00:01:0092:de:7b:2d:75:2f00:00:00:00:00:0200:00:00:00:00:0202:00:00:00:01:00148.0241248.000:00:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f3927.0000x0802010636.01061.715359e+09NaN0.0000742412000020240506172205file-logDistance-exp3dot5-nt-91dBm-fc3-minspeed10-maxspeed20-area1600-testhttp_load.pcap2024-05-13T14:35:06.849063wifi_sharkfest2024-05-13T14:35:52.128Z0.0.105.702024-05-10 22:53:21.181334016
9107TCPTCPradiotap:wlan_radio:wlan:llc:ip:tcp99231.715382e+092Data frame0x0020Data02:00:00:00:01:0092:de:7b:2d:75:2f00:00:00:00:00:0200:00:00:00:00:0202:00:00:00:01:00154.0241254.000:00:00:00:00:02$02:00:00:00:01:00$92:de:7b:2d:75:2f3928.0000x0802010636.01061.715359e+09NaN0.0000892412000020240506172205file-logDistance-exp3dot5-nt-91dBm-fc3-minspeed10-maxspeed20-area1600-testhttp_load.pcap2024-05-13T14:35:06.849063wifi_sharkfest2024-05-13T14:35:52.128Z0.0.105.702024-05-10 22:53:21.181422848